Recap @ Ontoweb3
Invitational Workshop on Database and Information
Systems Research
For Semantic Web and Enterprises
Amit Sheth & Robert Meersman
NSF Information & Data Management PI’s Workshop
Amit Shetth & Isabel Cruz
“Ask not what the Semantic Web Can
do for you, ask what you can do for
the Semantic Web”
Hans-Georg Stork, European Union
http://lsdis.cs.uga.edu/SemNSF
Challenges
SCALE and PERFORMANCE
Acceptable response time when you have millions
and billions of instances
• locking for sharing/storage management
• Semantic similarity, mappings, interoperability
(schema transformation/integration)
• indexing for queries (semistructured and
structured data)
• workflow for WS process
History
(partial, DB/IS centric)
• Semantic Data Modeling
M. Hammer and D. McLeod: "The Semantic Data Model: A Modelling Machanism for Data Base
Applications"; Proc.. ACM SIGMOD, 1978.
• Conceptual Modeling
Michael Brodie, John Mylopoulos, and Joachim W. Schmidt. On Conceptual Modeling. Springer Verlag, New
York, NY, 1984.
• So Far (Schematically) yet So Near (Semantically)
• Data Semantic: What, Where and How?
Meersman, Navathe, Rosenthal, Sheth,
• Semantic Interoperability on Web
many projects in 90s
– 1993 CIKM paper on multiple preexisiting ontologies
• Domain Modeling, Metadata, Context, Ontologies, Semantic
Information Brokering, Agents, Spatio-temporal-geographicimage-video-multimodal semantics
• Most of the above before “Semantic Web” term is coined
Context for Amicalola workshop
• Series of Workshops and upcoming conferences:
Lisbon (9/00), Hong Kong (5/01), Palo Alto (7/01),
Amsterdam (12/01); upcoming: WWW2002/ISWC
– Observation: visible lack of DB/IS involvement
• “Semantic Web – The Road Ahead,”
[Decker, Hans-Georg Stork, Sheth, … SemWeb’2001 at WWW10,
Hongkong, May 1, 2001. ]
• Semantic Web: Rehash or Research Goldmine
[Fensel, Mylopoulous, Meersman, Sheth, CooPIS’01]
• At Castel Pergine, Italy
Organization
•
•
•
•
20+ senior researchers/practitioners
2.5 days in Georgia Mountains
Proceedings of position papers (also talks)
Three workgroups: Application Pull
(Brodie/Dayal), Ontology (Decker/Kashyap) and Web
Services (Fensel/Singh)
• Upcoming -- report and special issue
• lsdis.cs.uga.edu/SemNSF/
Participants
Karl Aberer, LSIR, EPFL, Switzerland
Mike Brodie, Verizon
Isabel Cruz, The University of Illinois at Chicago
Umeshwar Dayal, Hewlett-Packard Labs
Stefan Decker, Stanford University
Max Egenhofer, University of Maine
Dieter Fensel, Vrije Universiteit Amsterdam
William Grosky,University of Michigan-Dearborn
Michael Huhns, University of South Carolina
Ramesh Jain, UC-San Diego, and Praja
Yahiko Kambayashi, Kyoto University
Vipul Kashyap, National Library of Medicine
Ling Liu, Georgia Institute of Technology
Frank Manola, The MITRE Corporation
Robert Meersman, Vrije Universiteit Brussel (VUB)
Amit Sheth, University of Georgia and Voquette
Munindar Singh, North Carolina State University
George Stork, EU
Rudi Studer, AIFB Universität Karlsruhe
Bhavani Thuraisingham, NSF-CISE-IIS
Michael Uschold, The Boeing Company
Medical metaphor
• Ontologies: anatomy
• Processes: physiology
• Applications: pathology 
Ontology Search
Compare/Similarity
Requirements/
Analysis
Ontology
Learning
Merge/
Refine/Assemble
Evaluation
Maintenance
Versioning
Creation/
Change
Consistency
Checking
Deployment
(e.g., Hypothesis Generation, Query)
DB Research in the Ontology LifeCycle
• Operations to compare Models/Ontologies
• Scalability/Storage Indexing of Ontologies
– DB approaches data model specific
– Need to support graph based data models
• Temporal Query Languages
Lots of work in Schema Integration/translation
Ontology WG: DB Research in the
Ontology LifeCycle II
• Schema Mapping
– Meta Model specific
– Representation of exceptions, e.g., tweety
– Specification of Inexact Schema Correspondences
• E.g., 40% of animals are 30% of humans
• Meta Model Transformations/Mappings (e.g.,
UML to RDF Schema)
Ontology WG: DB Research in the
Ontology LifeCycle III
• Ontology Versioning
– Collaborative editing
– Meta Model specific versioning
– Version of Schema/Meta Model
Transformations
Ontology WG: DB Research &
Semantic Interoperation
• Inference v/s Query Rewriting/Processing for Semantic Integration:
• E.g., RichPerson = (AND Person (> Salary 100))
• Can Query Processing/Concept Rewriting provide the same
functionality as inferences ? More efficiently ?
• Distributed Inferences and Loss of Information
• Query Languages for combining metadata and data queries
• Graph-based data models and query languages
• Schema Correspondences/Mappings
•Intensional Answers (Answers are descriptions, e.g. (AND Person (>
Salary 100)) instead of a list of all rich people)
•Semantic Associations (identification of meaningful relationships
between different types of instances)
Semantic Index
Semantic WS Scope
Worth pursuing
Std
Program
All
Formally self-described
currency.com
Amazon
html
Self-described
Hard code
People
Mike’s Humor
• Services vs. Ontologies
“Well done is better than well said.”
Ben Franklin
SWS– Fitting in and expanding IS/DB/DM: Or why Bhavani
& George should care?
Data => services, similar yet more challenging:
–
–
–
–
–
–
Modeling
Organizing collections
Discovery and comparison (reputation)
Distribution and replication
Access and fuse (composition)
Fulfillment
• Contracts, coordination versus transactions
• Quality: more general than correctness or precision
• Compliance
– Dynamic, flexible information security and trust.
Research Issues
• Conversational (state-based, event-based, history-based)
• Interoperability of conversational services – compose,
translate,
• Representations for services: programmatic self-description
• Commitments, contracts, negotiation
• Discovery, location, binding
• Compliance
• Cooperation
• Transactional workflow: rollback, roll-forward, semantic
exception handling, recovery
• Trustworthy service (discovery, provisioning, composition,
description)
• Security; privacy vs. personalization
• Quality-of-Service, w.r.t. various aspects, negotiable